import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
# from sphereModel.SphereConv import Sphere_Conv2d
from sphereModel.spherenet import SphereMaxPool2d
from sphereModel.spherenet import SphereConv2d as Sphere_Conv2d


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


def sphereconv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return Sphere_Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class SphereBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(SphereBasicBlock, self).__init__()
        self.conv1 = sphereconv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = sphereconv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class SphereBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(SphereBottleneck, self).__init__()
        self.conv1 = Sphere_Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = Sphere_Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = Sphere_Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class SphereResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(SphereResNet, self).__init__()
        self.conv1 = Sphere_Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = SphereMaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        # self.avgpool = nn.AvgPool2d(7, stride=1)
        # self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                Sphere_Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        # x = self.avgpool(x)
        # x = x.view(x.size(0), -1)
        # x = self.fc(x)

        return x
    
    def prune(self, sparsity, create_mask = True, prune_bias = False):
        first_layer = True
        if create_mask:
            self.masks = []
        masksid = -1
        for m in self.modules():
            if isinstance(m, Sphere_Conv2d) or isinstance(m, nn.Linear):
                if first_layer:
                    first_layer = False
                else:
                    if create_mask:
                        prune_k = int(m.weight.numel()*(sparsity))
                        val, _ = m.weight.view(-1).abs().topk(prune_k, largest = False)
                        prune_threshold = val[-1]
                        self.masks.append((m.weight.abs()<=prune_threshold).data)
                    masksid += 1
                    m.weight.data.masked_fill_(self.masks[masksid], 0) 
                    if prune_bias:
                        if create_mask:
                            prune_k = int(m.bias.numel()*(sparsity))
                            val, _ = m.bias.view(-1).abs().topk(prune_k, largest = False)
                            prune_threshold = val[-1]
                            self.masks.append((m.bias.abs()<=prune_threshold).data)
                        masksid += 1
                        m.bias.data.masked_fill_(self.masks[masksid], 0) 


def sphere_resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = SphereResNet(SphereBasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        pth = model_zoo.load_url(model_urls['resnet18'])
        # print(pth)
        model.load_state_dict(pth, strict=False)
    '''layers = list(model.children())[:-2]
    for layer in layers[:-4]:
        for p in layer.parameters():
            p.requires_grad = False
    enc = nn.Sequential(*layers)
    for name, value in enc.named_parameters():
        print(name,value.requires_grad)'''
    return model


def sphere_resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = SphereResNet(SphereBasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def sphere_resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = SphereResNet(SphereBottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def sphere_resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = SphereResNet(SphereBottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def sphere_resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(SphereBottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model
